MindMap Gallery EM algorithm
Machine learning algorithm, expectation maximum algorithm/EM algorithm, summary of principles.
Edited at 2021-01-23 10:44:27Avatar 3 centers on the Sully family, showcasing the internal rift caused by the sacrifice of their eldest son, and their alliance with other tribes on Pandora against the external conflict of the Ashbringers, who adhere to the philosophy of fire and are allied with humans. It explores the grand themes of family, faith, and survival.
This article discusses the Easter eggs and homages in Zootopia 2 that you may have discovered. The main content includes: character and archetype Easter eggs, cinematic universe crossover Easter eggs, animal ecology and behavior references, symbol and metaphor Easter eggs, social satire and brand allusions, and emotional storylines and sequel foreshadowing.
[Zootopia Character Relationship Chart] The idealistic rabbit police officer Judy and the cynical fox conman Nick form a charmingly contrasting duo, rising from street hustlers to become Zootopia police officers!
Avatar 3 centers on the Sully family, showcasing the internal rift caused by the sacrifice of their eldest son, and their alliance with other tribes on Pandora against the external conflict of the Ashbringers, who adhere to the philosophy of fire and are allied with humans. It explores the grand themes of family, faith, and survival.
This article discusses the Easter eggs and homages in Zootopia 2 that you may have discovered. The main content includes: character and archetype Easter eggs, cinematic universe crossover Easter eggs, animal ecology and behavior references, symbol and metaphor Easter eggs, social satire and brand allusions, and emotional storylines and sequel foreshadowing.
[Zootopia Character Relationship Chart] The idealistic rabbit police officer Judy and the cynical fox conman Nick form a charmingly contrasting duo, rising from street hustlers to become Zootopia police officers!
EM algorithm
Jensen's inequality
Many inequalities can be derived from Jenson's inequality
The derivation process
1. Establish the objective function through the maximum likelihood function
2. Find the lower bound of the likelihood function
The equal sign is established
x in log(x) is a constant, that is:
further analysis
3. Algorithm process
The process can be viewed as a coordinate rise
Algorithm application
GMM
Problem Description
algorithm process
Likelihood function for complete data
E step
The probability that the i-th sample belongs to j Gaussian distribution
M steps
Find the mean that maximizes the function
Find the variance that maximizes the function
Find the partial derivative, equal to 0
Find the parameters of the multinomial distribution that maximize the function
Lagrange multiplier method
Adjust parameters
Variance types are different variance matrix types
BIC evaluation value